Recording and estimating the gaze path of a user watching a screen is a mature technology opening new perspectives in terms of Human-Machine Interfaces. Such captures are till now mainly achieved using infrared video technologies in commercial systems.
[5] purport to describe a laptop computer product with integrated eye control, taking advantage of the reflection of infrared sources on the user's eyes to estimate the current gaze orientation.
Other, more experimental systems are physiologically based on the recording of the corneo-retinal potential by the means of electrodes positioned around the eye. Two electrodes are generally used to record the horizontal movements, two others catch the vertical motions and a last one is used as a reference. FIG. 1 shows an example of a horizontal capture setup.
Young et al [1] have purportedly shown that captured signals, namely ElectroOculoGram (EOG) signals, are linearly correlated to the eye motions.
Several commercial or academic systems embed dedicated amplifiers to measure and record the associated signal. “BIOPAC” systems for an example of generic biomedical amplifier, “BlueGain EOG Amplifier” developed by Cambridge Research Systems, and an Eye-movement Tracking System proposed by Deng [2].
Even if such systems were historically and mainly used for medical purposes [3], recent developments in video games and entertainment [4] prove their potential as a new way for users to interact with a machine.
The Boston College “EagleEyes” Project [6] is an example of taking advantage of the EOG to help users with severe physical disabilities to control a computer.
In [7], Bulling et al propose to use EOG signals to recognize users' activities by analyzing their eyes movements. Horizontal EOGs are processed with dedicated wavelet transforms and help to determine if the user is reading, writing or browsing while s/he is in front of her/his computer.
With the development of e-books, the improvements of TV-screens which are now able to satisfyingly display texts and web pages, it becomes apparent that reading comfort may not always be optimal and depends among others on the size of the text font used in the display. To adjust font size to individual users' needs, [5] requires an active interaction of the user with the machine like a deliberate click on an icon, or a specific eye motion to zoom on some part of a screen. The Single Line Reader algorithm implementation in [8] also makes use of deliberate head movements to control the speed and scrolling direction of a single line text display.
An improvement of ease of user interaction is thus desirable.